forked from mindspore-Ecosystem/mindspore
255 lines
7.6 KiB
Python
255 lines
7.6 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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@File : test_create_obj.py
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@Author:
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@Date : 2019-06-26
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@Desc : test create object instance on parse function, eg: 'construct'
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Support class : nn.Cell ops.Primitive
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Support parameter: type is define on function 'ValuePtrToPyData'
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(int,float,string,bool,tensor)
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"""
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import logging
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import numpy as np
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import pytest
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import mindspore.nn as nn
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from mindspore import context, ops, dtype
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from mindspore.common.api import jit
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from mindspore.common import Tensor, Parameter
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from mindspore.ops import operations as P
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from ...ut_filter import non_graph_engine
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log = logging.getLogger("test")
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log.setLevel(level=logging.ERROR)
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class Net(nn.Cell):
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""" Net definition """
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def __init__(self):
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super(Net, self).__init__()
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self.softmax = nn.Softmax(0)
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self.axis = 0
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def construct(self, x):
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x = nn.Softmax(self.axis)(x)
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return x
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# Test: Create Cell OR Primitive instance on construct
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@non_graph_engine
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def test_create_cell_object_on_construct():
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""" test_create_cell_object_on_construct """
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log.debug("begin test_create_object_on_construct")
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context.set_context(mode=context.GRAPH_MODE)
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np1 = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input_me = Tensor(np1)
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net = Net()
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output = net(input_me)
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out_me1 = output.asnumpy()
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print(np1)
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print(out_me1)
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log.debug("finished test_create_object_on_construct")
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# Test: Create Cell OR Primitive instance on construct
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class Net1(nn.Cell):
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""" Net1 definition """
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def __init__(self):
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super(Net1, self).__init__()
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self.add = P.Add()
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@jit
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def construct(self, x, y):
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add = P.Add()
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result = add(x, y)
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return result
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@non_graph_engine
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def test_create_primitive_object_on_construct():
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""" test_create_primitive_object_on_construct """
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log.debug("begin test_create_object_on_construct")
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context.set_context(mode=context.GRAPH_MODE)
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x = Tensor(np.array([[1, 2, 3], [1, 2, 3]], np.float32))
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y = Tensor(np.array([[2, 3, 4], [1, 1, 2]], np.float32))
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net = Net1()
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net.construct(x, y)
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log.debug("finished test_create_object_on_construct")
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# Test: Create Cell OR Primitive instance on construct use many parameter
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class NetM(nn.Cell):
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""" NetM definition """
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def __init__(self, name, axis):
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super(NetM, self).__init__()
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self.name = name
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self.axis = axis
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self.softmax = nn.Softmax(self.axis)
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def construct(self, x):
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x = self.softmax(x)
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return x
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class NetC(nn.Cell):
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""" NetC definition """
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def __init__(self, tensor):
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super(NetC, self).__init__()
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self.tensor = tensor
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def construct(self, x):
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x = NetM("test", 1)(x)
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return x
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# Test: Create Cell OR Primitive instance on construct
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@non_graph_engine
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def test_create_cell_object_on_construct_use_many_parameter():
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""" test_create_cell_object_on_construct_use_many_parameter """
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log.debug("begin test_create_object_on_construct")
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context.set_context(mode=context.GRAPH_MODE)
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np1 = np.random.randn(2, 3, 4, 5).astype(np.float32)
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input_me = Tensor(np1)
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net = NetC(input_me)
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output = net(input_me)
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out_me1 = output.asnumpy()
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print(np1)
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print(out_me1)
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log.debug("finished test_create_object_on_construct")
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class NetD(nn.Cell):
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""" NetD definition """
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def construct(self, x, y):
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concat = P.Concat(axis=1)
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return concat((x, y))
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# Test: Create Cell OR Primitive instance on construct
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@non_graph_engine
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def test_create_primitive_object_on_construct_use_kwargs():
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""" test_create_primitive_object_on_construct_use_kwargs """
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log.debug("begin test_create_primitive_object_on_construct_use_kwargs")
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context.set_context(mode=context.GRAPH_MODE)
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x = Tensor(np.array([[0, 1], [2, 1]]).astype(np.float32))
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y = Tensor(np.array([[0, 1], [2, 1]]).astype(np.float32))
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net = NetD()
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net(x, y)
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log.debug("finished test_create_primitive_object_on_construct_use_kwargs")
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class NetE(nn.Cell):
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""" NetE definition """
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def __init__(self):
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super(NetE, self).__init__()
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self.w = Parameter(Tensor(np.ones([16, 16, 3, 3]).astype(np.float32)), name='w')
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def construct(self, x):
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out_channel = 16
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kernel_size = 3
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conv2d = P.Conv2D(out_channel,
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kernel_size,
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1,
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pad_mode='valid',
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pad=0,
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stride=1,
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dilation=1,
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group=1)
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return conv2d(x, self.w)
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# Test: Create Cell OR Primitive instance on construct
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@non_graph_engine
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def test_create_primitive_object_on_construct_use_args_and_kwargs():
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""" test_create_primitive_object_on_construct_use_args_and_kwargs """
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log.debug("begin test_create_primitive_object_on_construct_use_args_and_kwargs")
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context.set_context(mode=context.GRAPH_MODE)
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inputs = Tensor(np.ones([1, 16, 16, 16]).astype(np.float32))
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net = NetE()
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net(inputs)
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log.debug("finished test_create_primitive_object_on_construct_use_args_and_kwargs")
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# Test: Create Cell instance in construct
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class SubCell(nn.Cell):
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def __init__(self, t):
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super(SubCell, self).__init__()
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self.t = t
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def construct(self):
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return ops.typeof(self.t)
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class WrapCell(nn.Cell):
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def construct(self, t):
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type_0 = ops.typeof(t)
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type_1 = SubCell(t)()
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return type_0, type_1
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def test_create_cell_with_tensor():
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"""
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Feature: Raise exception while create Cell(that init use tensor input) in construct.
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Description: None
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Expectation: TypeError.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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t = Tensor(np.zeros((2, 2), np.float), dtype.float32)
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with pytest.raises(TypeError):
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print(WrapCell()(t))
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def test_create_grad_operation():
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"""
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Feature: Create instance for GradOperation.
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Description: Create instance for GradOperation, and use it.
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Expectation: No exception.
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"""
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class NetInner(nn.Cell):
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def __init__(self):
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super(NetInner, self).__init__()
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self.mul = P.Mul()
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self.p = Parameter(Tensor(np.array([1.0], np.float32)), name='p')
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def construct(self, x, y):
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z = x * self.p
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out = self.mul(y, z)
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return out
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class GradNetWrtInputsAndParams(nn.Cell):
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def __init__(self, net):
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super(GradNetWrtInputsAndParams, self).__init__()
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self.net = net
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self.params = self.net.trainable_params()
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def construct(self, x, y):
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gradient_function = ops.GradOperation(get_all=True, get_by_list=True)(self.net, self.params)
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return gradient_function(x, y)
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context.set_context(mode=context.GRAPH_MODE)
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a = Tensor([[1, 2, 3]], dtype.float32)
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b = Tensor([[7, 8, 9], [10, 11, 12], [13, 14, 15]], dtype.float32)
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GradNetWrtInputsAndParams(NetInner())(a, b)
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